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EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks.


ABSTRACT:

Motivation

An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene-gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the potential to enrich for relevant genes and stabilize classification.

Results

We develop two new expression-epistasis centrality methods that incorporate interaction prior knowledge. The first extends our SNPrank (EpistasisRank) method by incorporating a gene-wise prior knowledge vector. This prior knowledge vector informs the centrality algorithm of the inclination of a gene to be involved in interactions by incorporating functional interaction information from the Integrative Multi-species Prediction database. The second method extends Katz centrality to expression-epistasis networks (EpistasisKatz), extends the Katz bias to be a gene-wise vector of main effects and extends the Katz attenuation constant prefactor to be a prior-knowledge vector for interactions. Using independent microarray studies of major depressive disorder, we find that including prior knowledge in network centrality feature selection stabilizes the training classification and reduces over-fitting.

Availability and implementation

Methods and examples provided at https://github.com/insilico/Rinbix and https://github.com/insilico/PriorKnowledgeEpistasisRank.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Parvandeh S 

PROVIDER: S-EPMC7963083 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Publications

EpistasisRank and EpistasisKatz: interaction network centrality methods that integrate prior knowledge networks.

Parvandeh Saeid S   McKinney Brett A BA  

Bioinformatics (Oxford, England) 20190701 13


<h4>Motivation</h4>An important challenge in gene expression analysis is to improve hub gene selection to enrich for biological relevance or improve classification accuracy for a given phenotype. In order to incorporate phenotypic context into co-expression, we recently developed an epistasis-expression network centrality method that blends the importance of gene-gene interactions (epistasis) and main effects of genes. Further blending of prior knowledge from functional interactions has the pote  ...[more]

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